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Model structure
A deliberate, navigable model with explicit relationships and a constrained surface for AI.
Productized assessment · Power BI
Find out why Copilot can answer the question—and still get the business meaning wrong.
Refinity traces one consequential use case from business language to model logic, security, and evaluation evidence. You leave with a defensible readiness verdict and a sequenced remediation plan—not a longer prompt library.
No pricing published · No model upload required for the scorecard
The mechanism
Dashboard-ready is not the same as AI-ready.
A dashboard constrains interpretation through selected visuals, filters, navigation, and author intent. The user sees a curated answer path.
Natural-language analytics asks the system to choose that path: which field, measure, relationship, filter, and time context represents the question. If several choices are technically valid but semantically different, a plausible answer can still be the wrong business answer.
Microsoft identifies model design, complexity, naming, organization, and linguistic context as factors in unexpected output—and states that responses are not guaranteed correct or repeatable. Microsoft product documentation
Signals worth investigating
- The same question produces different measures, filters, or time periods.
- A dashboard looks correct, but users must know which visual or slicer to trust.
- Business terms such as revenue, active customer, or year to date have several plausible meanings.
- AI answers cannot be traced to an approved metric, grain, calendar, or source.
- Security was tested in reports, but not through the intended AI interaction.
- Model or instruction changes ship without a repeatable regression set.
A strong fit
- You have a consequential Power BI use case and identifiable user group.
- The model exists, but answer consistency or traceability is uncertain.
- Business and technical owners can review definitions and evidence.
- You want a bounded decision before a larger Copilot or agent rollout.
Not the right engagement
- You need a generic Copilot demo without a real business question.
- No semantic model or accountable data owner exists yet.
- The goal is to certify or guarantee every generated answer.
- The immediate need is broad platform implementation rather than readiness evidence.
Deliberately bounded scope
01
One consequential business use case
02
One priority semantic model
03
One target user group
04
Up to 20 representative questions
What Refinity inspects
Seven layers between a question and a defensible answer.
The model is necessary, but it is only one part of the answer system. The assessment follows the whole chain.
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Business terminology
Names, definitions, synonyms, and ownership that match how the target users ask questions.
03
Measures and data foundations
Trusted explicit measures with traceable source, grain, calendar, filters, and ownership.
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AI context and instructions
A focused AI data schema plus instructions that resolve business language without contradicting the model.
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Verified answers
Authoritative responses for high-value questions, with known scope and maintenance ownership.
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Security and governance
Permissions, sensitive fields, ownership, and change controls tested through the AI experience.
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Evaluation and lifecycle controls
A repeatable golden-question set, regression checks, owners, and release decisions.
Engagement process
From question set to remediation sequence.
The assessment distinguishes documented intent from reproducible evidence, and product configuration from business-policy decisions.
- 01FrameChoose the use case, target users, semantic model, decision stakes, and representative questions.
- 02InspectTrace model structure, measures, vocabulary, AI context, permissions, and existing controls.
- 03TestRun the question set, inspect selected fields and filters, record failures, and reproduce expected answers.
- 04DecideSeparate fixable model gaps from business-policy ambiguity and product-channel constraints.
- 05SequenceDeliver the evidence register, recommendation, and prioritized 30/60/90-day remediation roadmap.
Concrete deliverables
A decision package your team can act on.
Each output ties a claim to evidence, an owner, and a recommended next step. It is designed to survive handoff to BI, data, security, and business teams.
- Readiness heatmap
- Findings and evidence register
- Metric, source, grain, calendar, filter, and ownership map
- AI data-schema recommendations
- AI-instruction recommendations
- Verified-answer plan
- Golden-question evaluation set
- Security and governance risks
- Copilot vs. Fabric Data Agent vs. custom-agent recommendation
- Prioritized remediation roadmap
Illustrative sample
Executive readiness heatmap
Model structure68%
Measures31%
AI context44%
Security22%
Evaluation11%
See the standard before you engage
Browse a complete synthetic report.
The sample shows how findings, business risk, answer contracts, failed questions, target architecture, and remediation sequencing fit together. It is illustrative—not a client result.
Open the sample reportSelected founder experience · Client context anonymized
Evidence work built in Fortune-100 consumer-goods analytics.
Caught a nine-figure mixed-grain double count before executive reporting.
Identified hundreds of millions in misclassified revenue caused by plausible but non-equivalent filters.
Surfaced a double-digit-million discrepancy between valid calendar conventions.
Quantified double-digit variation between plausible definitions of one business metric.
Reduced remaining migration audit scope approximately 95% in one cross-functional triage session.
Built Git-backed Power BI delivery, PR review, and an approximately 60-rule model-quality gate.
This experience includes semantic-model standards, verification artifacts, and operating controls. It does not imply a completed client Copilot implementation.
FAQ
Before you decide.
Is this a Copilot implementation?
No. It is a bounded readiness assessment that determines what must be clarified, tested, or governed before a consequential use case moves forward. The output can support a later implementation decision.
Do you need our full Power BI estate?
No. The initial scope is deliberately narrow: one consequential use case, one priority semantic model, one target user group, and up to 20 representative business questions.
Does a high score guarantee correct Copilot answers?
No. Microsoft documents that Copilot output is nondeterministic and not guaranteed correct. Readiness controls reduce avoidable ambiguity and make answers easier to evaluate; they do not make a probabilistic system infallible.
Will Refinity upload our model through the public scorecard?
No. The public scorecard does not accept files, and its answers remain in the browser tab. Any later evidence exchange would be agreed separately with appropriate access and confidentiality controls.
Do we need AI data schemas, AI instructions, and verified answers?
Not automatically. Each control addresses a different problem and some current capabilities have preview limitations. The assessment recommends the smallest useful combination for the chosen questions and delivery channel.
Is pricing published?
Not yet. Scope depends on model complexity, evidence availability, security roles, and the questions being evaluated. A short technical review establishes whether the bounded assessment is a fit.
Technical review
Bring the question your AI must answer reliably.
Refinity will determine whether this bounded assessment fits the decision you need to make. Do not submit confidential information through the public form.